Automatic Modulation Classification for MIMO-OFDM Systems with Imperfect Timing Synchronization

Yuan, Xiaoyu; Li, Yongzhao; Gao, Mingzhong; Li, Tao; Zhang, Hailin

doi:10.1109/vtcfall.2017.8287963

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…The MC problem for MIMO systems employing OFDM under imperfect timing synchronization scenarios is studied in [ 75 ]. The proposed algorithm first uses the HOC of the received signal to extract the unique features, which show the robustness to STO.…”

Section: Artificial Intelligence-based Approach To MCmentioning

confidence: 99%

“…Various MC algorithms for the OFDM systems were carried out in [ 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 ]. The algorithms for multiple-input multiple-output and OFDM (MIMO-OFDM) systems based on deep neural network (DNN) and Gibbs sampling are investigated in [ 44 ].…”

Section: Introductionmentioning

confidence: 99%

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A Survey of Blind Modulation Classification Techniques for OFDM Signals

Kumar

Majhi

Gui

et al. 2022

Sensors

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Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed.

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Section: Artificial Intelligence-based Approach To MCmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey of Blind Modulation Classification Techniques for OFDM Signals

Kumar

Majhi

Gui

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…From 7, we can easily differentiate π/4-QPSK and the rest of modulations as shown in Table I. At the final-stage, to identify the remaining modulation formats, we use eight-order cumulant with four conjugations, which can be expressed as [7] . From the received signal y e [n], we can find the power for the i-th constellation point as P ri ≈ ρ aP xi , assuming that the power harvested from the passband and circuit noise is negligible, where a is the fading power gain and P xi is the transmit power for the i-th constellation point.…”

Section: Signal Modelmentioning

confidence: 99%

A New Receiver Design: Simultaneous Wireless Power Transfer With Modulation Classification

Gupta

Krikidis

2020

2020 IEEE Wireless Power Transfer Conference (WPTC)

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“…Whereas FB algorithms are easy to implement and computational complexity is much lower than LB algorithms [11]- [16]. The higherorder cumulants and moments-based MC algorithms employ threshold values to classify the modulation formats [11]- [13]. The cyclic cumulants-based methods discussed in [14]- [16] are more robust and use the non-zero cycle frequency position to identify the modulation schemes.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Wireless Power Transfer and Modulation Classification

Gupta

Krikidis

2021

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)

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This work proposes a new simultaneous wireless power transfer and modulation classification (SWPTMC) scheme appropriate for internet of things (IoT) applications. The problem of SWPTMC is investigated for various modulation formats, i.e., quadrature phase-shift-keying (QPSK), π/4-QPSK, offset QPSK (OQPSK), 16-pulse amplitude modulation (16-PAM), 16quadrature amplitude modulation (16-QAM), and minimum shift keying (MSK). We propose three receiver architectures, i.e., an integrated receiver, a separate receiver with power splitting (PS), and a separate receiver with energy harvesting (EH)-based classification; all the architectures are studied under a nonlinear model with a certain sensitivity and saturation level. Also, we derive the average harvested power over a Rayleigh fading channel for the different modulation formats. Two different approaches are used for the blind modulation classification (MC) algorithm: one for the intermediate frequency signal and the other for the baseband signal. Both the MC algorithms are based on the higher-order cumulants and cyclic cumulants of the received signal. The cyclic cumulants use the non-zero cycle frequency position, while the cumulants use threshold values for classifying modulation formats. Monte Carlo simulations are used to evaluate the performance of the proposed SWPTMC schemes. The results show that we can simultaneously harvest power without much affecting the classifier performance. Moreover, with an integrated receiver, we can simultaneously perform MC and harvest power without the requirement of PS circuit.

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Automatic Modulation Classification for MIMO-OFDM Systems with Imperfect Timing Synchronization

Cited by 6 publications

References 15 publications

A Survey of Blind Modulation Classification Techniques for OFDM Signals

A Survey of Blind Modulation Classification Techniques for OFDM Signals

A New Receiver Design: Simultaneous Wireless Power Transfer With Modulation Classification

Simultaneous Wireless Power Transfer and Modulation Classification

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